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Guidewire

Cloud-based SaaS platform for P&C insurers

Senior Machine Learning Platform Engineer

Applied Machine LearningUpdated on Nov 12
Full-timeSan Mateo, CA, USA

About the Job

Requirements

  • Demonstrated ability to embrace artificial intelligence and apply it to the current role, using data-driven insights to drive innovation, productivity, and continuous improvement.
  • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
  • At least 10 years of software engineering experience, including at least 5 years working on machine-learning platforms or infrastructure.
  • Expertise in building large-scale distributed systems and microservices.
  • Strong programming skills in Python, Go, or Java.
  • Experience with containerization and orchestration, such as Docker and Kubernetes.
  • Advanced experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks.
  • Experience with a cloud platform such as AWS, GCP, or Azure.
  • Experience with statistical learning algorithms such as GLM, XGBoost, and Random Forest, and deep learning such as neural networks and transformers.
  • Strong communication, leadership, and problem-solving skills.

Responsibilities

  • Architect and guide the design of a scalable, secure machine-learning platform supporting the full machine-learning lifecycle, from data ingestion to model monitoring.
  • Design and implement infrastructure for model training, hyperparameter tuning, experiment tracking, and model registry.
  • Orchestrate machine-learning workflows using tools such as Kubeflow, SageMaker, MLflow, or similar tools.
  • Collaborate with Data Scientists, MLOps engineers, Data Engineers, and Product Engineering to define best practices for reproducibility, governance, and CI/CD for machine learning.
  • Partner with Data Engineers to build robust data pipelines for model-ready datasets.
  • Optimize machine-learning workload performance across compute and storage layers using cloud-native and open-source solutions.
  • Lead technical discussions, mentor junior engineers, and help set the technical vision for the machine-learning platform roadmap.
  • Ensure compliance with security, privacy, and regulatory requirements throughout the machine-learning lifecycle.
  • Lead the design and implementation of core machine-learning platform components, collaborate with cross-functional teams to deliver scalable solutions, and establish best practices for machine-learning operations.

Desired Qualifications

  • Experience with real-time model inference and streaming machine-learning pipelines.
  • Deep knowledge of model governance, reproducibility, and monitoring.
  • Understanding of model performance metrics and drift detection.
  • Exposure to feature stores such as Feast and Tecton, and workflow tools such as Airflow and Argo.
  • Familiarity with regulatory considerations including model auditability, interpretability, and data privacy laws such as CCPA and GDPR.
  • Experience with real-time data pipelines such as Kafka, Flink, and Spark Structured Streaming.
  • Experience using TeamCity and Terraform for infrastructure setup and CI/CD.
  • Insurance industry or related experience in banking or finance.

About the Company

Guidewire

Cloud-based SaaS platform for P&C insurers

Guidewire provides software for the property and casualty (P&C) insurance industry. Its platform is a cloud-based suite that helps insurers run core operations such as underwriting, policy administration, billing, and claims, with added analytics and AI features. Customers subscribe to the software as a service (SaaS) and pay for cloud access, implementation, and ongoing support. The company differentiates itself by its deep specialization in P&C, a large number of successful implementations (about 1,600), a track record of industry recognition (awards and a strong position in Gartner’s Magic Quadrant for P&C Core Platforms in North America), and an integrated set of digital, core, analytics, and AI tools designed to modernize insurers’ core systems. Its goal is to help P&C insurers modernize their technology, improve customer experiences, and grow their businesses through a cloud-based platform.

Company size
1,001-5,000
Company stage
IPO
Headquarters
San Mateo, CA
Founded
2001

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